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Google’s Powerful Artificial Intelligence Spotlights a Human Cognitive Glitch

     
     Words can have a powerful effect on people, even when they’re generated by an unthinking machine.
     People are so accustomed to presuming that fluent language comes from a thinking, feeling human that evidence to the contrary can be difficult to comprehend. How are people likely to navigate this relatively uncharted territory? Because of a persistent tendency to associate fluent expression with fluent thought, it is natural – but potentially misleading – to think that if an artificial intelligence model can express itself fluently, that means it also thinks and feels just like humans do.
     As a result, it is perhaps unsurprising that a former Google engineer recently claimed that Google’s AI system LaMDA has a sense of self because it can eloquently generate text about its purported feelings. This event and
      led to a
      of rightly skeptical
      and
      about the claim that computational models of human language are sentient, meaning capable of thinking, feeling, and experiencing.
     The question of what it would mean for an AI model to be sentient is actually quite complicated (
     ), and our goal in this article is not to settle it. But as
     
     , we can use our work in cognitive science and linguistics to explain why it is all too easy for humans to fall into the cognitive trap of assuming that an entity that can use language fluently is sentient, conscious, or intelligent.
     Text generated by models like Google’s LaMDA can be hard to distinguish from text written by humans. This impressive achievement is a result of a decadeslong program to build models that generate grammatical, meaningful language.
     The first computer system to engage people in dialogue was psychotherapy software called Eliza, built more than half a century ago. Credit: Rosenfeld Media/Flickr,
     Early versions dating back to at least the 1950s, known as n-gram models, simply counted up occurrences of specific phrases and used them to guess what words were likely to occur in particular contexts. For instance, it’s easy to know that “peanut butter and jelly” is a more likely phrase than “peanut butter and pineapples.” If you have enough English text, you will see the phrase “peanut butter and jelly” again and again but might never see the phrase “peanut butter and pineapples.”
     Today’s models, sets of data and rules that approximate human language, differ from these early attempts in several important ways. First, they are trained on essentially the entire internet. Second, they can learn relationships between words that are far apart, not just words that are neighbors. Third, they are tuned by a huge number of internal “knobs” – so many that it is hard for even the engineers who design them to understand why they generate one sequence of words rather than another.
     The models’ task, however, remains the same as in the 1950s: determine which word is likely to come next. Today, they are so good at this task that almost all sentences they generate seem fluid and grammatical.
     We asked a large language model,
     , to complete the sentence “Peanut butter and pineapples___”. It said: “Peanut butter and pineapples are a great combination. The sweet and savory flavors of peanut butter and pineapple complement each other perfectly.” If a person said this, one might infer that they had tried peanut butter and pineapple together, formed an opinion and shared it with the reader.
     But how did GPT-3 come up with this paragraph? By generating a word that fit the context we provided. And then another one. And then another one. The model never saw, touched or tasted pineapples – it just processed all the texts on the internet that mention them. And yet reading this paragraph can lead the human mind – even that of a Google engineer – to imagine GPT-3 as an intelligent being that can reason about peanut butter and pineapple dishes.
     The human brain is hardwired to infer intentions behind words. Every time you engage in conversation, your mind automatically constructs a mental model of your conversation partner. You then use the words they say to fill in the model with that person’s goals, feelings and beliefs.
     The process of jumping from words to the mental model is seamless, getting triggered every time you receive a fully fledged sentence. This cognitive process saves you a lot of time and effort in everyday life, greatly facilitating your social interactions.
     However, in the case of AI systems, it misfires – building a mental model out of thin air.
     A little more probing can reveal the severity of this misfire. Consider the following prompt: “Peanut butter and feathers taste great together because___”. GPT-3 continued: “Peanut butter and feathers taste great together because they both have a nutty flavor. Peanut butter is also smooth and creamy, which helps to offset the feather’s texture.”
     The text in this case is as fluent as our example with pineapples, but this time the model is saying something decidedly less sensible. One begins to suspect that GPT-3 has never actually tried peanut butter and feathers.
     A sad irony is that the same cognitive bias that makes people ascribe humanity to GPT-3 can cause them to treat actual humans in inhumane ways. Sociocultural linguistics – the study of language in its social and cultural context – shows that assuming an overly tight link between fluent expression and fluent thinking can lead to bias against people who speak differently.
     For instance, people with a foreign accent are often
      and are less likely to get the jobs they are qualified for. Similar biases exist against
      that are not considered prestigious,
      in the U.S., against
     , and against people with speech impediments
     .
     These biases are deeply harmful, often lead to racist and sexist assumptions, and have been shown again and again to be unfounded.
     Will AI ever become sentient? This question requires deep consideration, and indeed philosophers have
      it
     . What researchers have determined, however, is that you cannot simply trust a language model when it tells you how it feels. Words can be misleading, and it is all too easy to mistake fluent speech for fluent thought.
     Authors:
     Contributors:
     This article was first published in
     .
     I would be interested in how GPT-3 would respond if you asked it the old joke, “Why is a mouse?”
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Jul 04th, 2022
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